This study investigates the current landscape of generative AI and LLM applications in architecture, engineering, and construction (AEC), focusing on trends, practical implications, educational strategies, and imperatives for upskilling. Employing a six-stage systematic review sourced from Google Scholar, Scopus and Web of Science, 120 papers were analyzed to provide a comprehensive understanding of the role of these technologies in shaping the future of the AEC industry. By addressing these objectives, the research contributes to enhancing knowledge about the potential impacts of generative AI and LLMs on the AEC industry and provides insights into strategies for leveraging these technologies effectively. This study underscores the transformative impact of AI and advanced technologies on the AEC sector and education. By enhancing learning experiences and optimizing construction processes, AI fosters personalized education and efficient project management. The study’s significance lies in its identification of necessary skills and competencies for professionals, ensuring effective AI integration. Implications include the need for continuous professional development, formal education, and practical training to leverage AI’s potential fully. This paves the way for sustainable, intelligent infrastructure and accessible, adaptive learning environments, driving innovation and efficiency in both fields.

Thermal comfort is a complex issue in the built environment due to the physiological and psychological differences of each individual in a building. There is a growing worry over the environmental implications of energy use as a result of the warming of the global climate and the growth in the number of instances of extreme weather events. Many review articles have been written, but these reviews have focused on a specific aspect of occupant behavior and thermal comfort. To research the trends of thermal comfort and energy, this research adopted mixed reviews, i.e., quantitative and qualitative, to understand the state-of-the-art factors affecting the thermal comfort of occupants concerning energy, different occupant modeling approaches, functions, and limitations. The in-depth qualitative discussion provides deeper insights into the impacts of occupant behaviors, factors affecting thermal comfort, and occupant behavior modeling approaches. This study classified occupant behaviors into five categories: occupant characteristics, perceptions of the occupant, realistic behaviors, heat gain, and occupant interactions with the system. It also went further to classify the factors affecting the thermal comfort of users based on past works of literature. These include structural, environmental, and human factors. It was concluded that factors that have the most significant impact on energy are human, structural, and environmental factors, respectively. In addition, most of the occupant behavior modeling approaches that have been used in past studies have pros and cons and cannot accurately predict human behaviors because they are stochastic. Future research should be conducted on thermal comfort for different building functions by examining the varied activity intensity levels of users, especially in educational or commercial buildings. Additionally, a proper investigation should be carried out on how thermal insulation of structural members influences thermal comfort. These should be compared in two similar buildings to understand occupant behavioral actions and energy consumption.

With the emergence of Building Information Modelling (BIM) as central to construction design, planning, execution and maintenance, integration into the entire infrastructure sustainability process is imperative for achieving sustainable development. Despite its immense benefit of aiding compliance to sustainable construction, potential barriers continue to widen the gap in implementation. Therefore, this study adopts the “interpretive structural modelling approach” to advance a ranked structure of the interrelatedness of the barriers to integrating BIM in buildings sustainability assessment. The “Matrice d’Impacts croises-multipication applique a classement analysis (MICMAC)” was utilised to categorise the identified adoption barriers in the model. The identified barriers and relationship with themselves are valuable in discussing the challenges to BIM-based LCA and developing policies and design decisions to drive the process further. Further, it adds to the emerging discussion of BIM from the life cycle sustainability assessment perspective for infrastructure. The findings are critical for policy, stakeholders and extending the body of knowledge.

Utilising emerging innovative technologies and systems to improve construction processes in an effort towards digitalisation has been earmarked as critical to delivering resilience and responsive infrastructure. However, successful implementation is hindered by several challenges. Hence, this study evaluates the challenges facing the adoption of unmanned aerial vehicles towards the digitalisation of the built environment. The study adopted a quantitative survey of built environment stakeholders in developed and developing economies. A total of 161 completely filled forms were received after the survey, and the data were analysed using descriptive analysis and inferential statistics. The study’s findings show that there are different barriers experienced between developed and developing countries in the adoption of drones towards digitalising construction processes in the built environment. Moreover, economic/cost-related factors were identified as the most critical barriers to the adoption of drones, followed by technical/regulatory factors and education/organisation-related factors. The findings can assist the built environment in reducing the impact of these barriers and could serve as a policy instrument and helpful guidelines for governmental organisations, stakeholders, and others.

Purpose: Rapid urbanisation and recent shock events have reiterated the need for resilient infrastructure, as seen in the pandemic. Yet, knowledge gaps in construction robotics and human–robot teams (HRTs) research limit maximising these emerging technologies’ potentials. This paper aims to review the state of the art of research in this area to identify future research directions in HRTs able to aid the resilience and responsiveness of the architecture, engineering and construction (AEC) sector. Design/methodology/approach: A total of 71 peer-reviewed journal articles centred on robotics and HRTs were reviewed through a quantitative approach using scientometric techniques using Gephi and VOSviewer. Research focus deductions were made through bibliometric analysis and co-occurrence analysis of reviewed publications. Findings: This study revealed sparse and small research output in this area, indicating immense research potential. Existing clusters signifying the need for further studies are on automation in construction, human–robot teaming, safety in robotics and robotic designs. Key publication outlets and construction robotics contribution towards the built environment’s resilience are discussed. Practical implications: The identified gaps in the thematic areas illustrate priorities for future research focus. It raises awareness on human factors in collaborative robots and potential design needs for construction resilience. Originality/value: Rapid urbanisation and recent shock events have reiterated the need for resilient infrastructure, as seen in the pandemic. Yet, knowledge gaps in construction robotics and HRTs research limit maximising these emerging technologies’ potentials. This paper aims to review the state of the art of research in this area to identify future research directions in HRTs able to aid the resilience and responsiveness of the AEC sector.

Increasing demand for infrastructure amidst the surge in the urbanization of cities and newly emerging commercial nerves has spurred the need to reinvent and rethink traditional approaches for delivering infrastructure. This has been identified as even more critical given the global drive and discourse on the sustainability of the construction sector and its health and safety performance. Given the potential gains of adopting construction automation and AI in infrastructure delivery, stakeholders’ convincing appreciation of its benefit is vital to its widespread adoption in the AEC sector. This explored and evaluated the critical benefits of integrating automation in construction processes in the architectural, engineering, and construction sector and the use of artificial intelligence (AI) in driving its systems and workflows. The study adopts an interpretive structural modeling approach based on interviews of construction stakeholders in diverse countries to develop a hierarchical model of the interrelationships of the benefits. Furthermore, the Matrice d’Impacts croises-multiplication applique a classement analysis (MICMAC) was used to categorize the benefits. Highlighted perceived benefits such as improved project quality, simplification of construction tasks, workflow improvements, and safety performance, amongst others, were fractionalized into levels. The study’s findings are critical in satisfying a cost-benefit index of adopting automation and AI in the AEC sector. The results provide recommendations on effective approaches pivotal to driving automation and AI for practice and research. This is of further importance to construction stakeholders, policymakers, and local authorities in building strategies and roadmaps for proper integration of these systems and widespread adoption.